for \(G,G^{\prime }\in \mathcal {G}\) , where \(\phi : \mathcal {G} \rightarrow \mathbb {R}^{N}\)  is an
embedding, called a feature map and \(\langle -,-\rangle \)  is the
standard inner product in \(\mathbb {R}^{N}\) .
The kernel method was introduced in the field
of machine learning
[1]}, [2]}.
Since then quite a few graph kernels have been proposed for graphs and
labelled graphs.
Such graph kernels are proposed to answer two
often-encountered questions, in the context of graphs. Namely, “How
similar are two nodes in a given graph?” and
“How similar are two graphs to each other?”.
More details on graph kernels can be
obtained from the survey paper [3]}.
